code: def update_learning_rate_schedule_parameters(
      params, epochs, global_batch_size, steps_per_epoch
  ):
      """Updates params that are related to the learning rate schedule."""
      # Learning rate is proportional to the batch size
      params["adjusted_learning_rate"] = (
          params["learning_rate"]
          * global_batch_size
          / _DEFAULT_BATCH_SIZE
      )
  
      if "lr_warmup_init" in params:
          params["adjusted_lr_warmup_init"] = (
              params["lr_warmup_init"]
              * global_batch_size
              / _DEFAULT_BATCH_SIZE
          )
  
      params["lr_warmup_step"] = int(
          params["lr_warmup_epoch"] * steps_per_epoch
      )
      params["first_lr_drop_step"] = int(
          params["first_lr_drop_epoch"] * steps_per_epoch
      )
      params["second_lr_drop_step"] = int(
          params["second_lr_drop_epoch"] * steps_per_epoch
      )
      params["total_steps"] = epochs * steps_per_epoch
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/model/utils/optimizers.py
imports: from absl import logging
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: update_learning_rate_schedule_parameters(params, epochs, global_batch_size, steps_per_epoch) without return types